Information Aggregation and Manipulation in an Experimental Market – Robin Hanson, Ryan Oprea, David Porter
This study looks at price accuracy in experimental (laboratory) markets, where there are price manipulators. The overall finding is that non-manipulative traders compensate for the bias inherent in the offers from manipulators, by setting a different threshold for trading. The authors acknowledge that the “identification of manipulation in the field is difficult” and empirical evidence is scarce and tenuous. Hence the need for a controlled, laboratory experiment. For background on the experiments, please refer to the original paper.
There were two parts to this experiment. In the Replication Treatment, there were no manipulators present, and in the Manipulation Treatment, one-half of the participants were given an incentive to increase the median price at the close of the market. All participants knew that half of their number had this incentive to manipulate, and they knew the direction that the manipulation would take (upward). Where the non-manipulative traders knew that the manipulative traders would attempt to bid up the price in the market, they lowered their threshold for accepting offers, effectively counteracting the manipulative influence in the market. This makes intuitive sense, but only in the case where the non-manipulative traders know the direction of the manipulation.
In my previous post, I indicated that it would be necessary for the non-manipulative (“informed”) traders to know which direction the manipulators would try to move the market. Robin Hanson commented that this is not necessary. I think he is wrong, now, but he was right when this paper was written! I think the authors are saying that it is required. In fact, in the paper, they go a step further and allow all participants to know the strength of the incentive to manipulate. We should keep in mind that, while this experiment demonstrates the concept of market manipulation and whether it can have a persistent effect on market prices, it is a pretty simple, controlled example. The real question is whether it can be generalized to more complex, real-world situations.